Safety control method and system based on environmental risk assessment for intelligent connected vehicle

ABSTRACT

Embodiments of the present application disclose a safety control method and a safety control system based on environmental risk assessment for an intelligent connected vehicle. The method includes: when a vehicle is in an automatic driving mode, acquiring environmental parameter information of the vehicle in a current driving environment; determining a target driving control parameter which meets a preset safe driving condition under the current environmental parameter; and managing a current automatic driving level of the vehicle by using the target driving control parameter.

CROSS-REFERENCE TO RELATED APPLICATION

The present application claims priority of Chinese Patent ApplicationNo. 202110363690.7, filed to the CNIPA on Apr. 2, 2021, and entitled“Safety Control Method and System based on Environmental Risk Assessmentfor Intelligent Connected Vehicle”, the contents disclosed in theapplication mentioned above are hereby incorporated as a part of thisapplication.

TECHNICAL FIELD

Embodiments of the present application relate to the field of automaticdriving, particularly a safety control method and a safety controlsystem based on environmental risk assessment for the intelligentconnected vehicle.

BACKGROUND

With the development and popularization of automatic driving, the safetyof autonomous vehicles has attracted people's attention. In relatedtechnologies, an automatic driving scheme that the safe speed ofvehicles can be set based on the number of historical accidents on-roadsections is proposed. Since historical accident data is challenging tocount on a large scale and the safety index is not comprehensive, thisscheme is not implemented. In addition, a safe driving control methodfor automatic vehicles based on current road state information andcurrent traffic roadblock information is also proposed. Current weatherinformation during driving, current sun azimuth information, and currenttime information are obtained from the current road state information.Still, if only such information is used for vehicle decelerationcontrol, the operation action is too single, and vehicle safety that canbe improved is limited.

SUMMARY

To solve any of the above technical problems, embodiments of the presentapplication provide a safety control method and a safety control systembased on environmental risk assessment for the intelligent connectedvehicle.

To achieve the purpose of the embodiments of the present application, inan embodiment of the present application, a safety control method basedon environmental risk assessment for the intelligent connected vehiclesis provided, including:

when a vehicle is in the automatic driving mode, acquiring environmentalparameter information of the vehicle in the current driving environment.

determining a target driving control parameter that meets a preset safedriving condition under the current environmental parameter; and

managing a current automatic driving level of the vehicle by using thetarget driving control parameter.

A safety control system based on environmental risk assessment for theintelligent connected vehicles includes:

an acquisition module configured to acquire environmental parameterinformation of the vehicle in the current driving environment when thevehicle is in an automatic driving mode;

a determination module configured to determine a target driving controlparameter that meets a preset safe driving condition under the currentenvironmental parameter; and

a management module configured to manage the current automatic drivinglevel of the vehicle by using the target driving control parameter.

A storage medium with a computer program stored therein, wherein whenbeing run, the computer program is configured to perform to theabove-mentioned method.

An electronic device includes a memory and a processor where a computerprogram is stored in the memory, and the processor is configured to runthe computer program to perform the above-mentioned method.

One of the above technical solutions has the following advantages orbeneficial effects.

The driving control parameter in the current environmental conditionwhich meets the safe driving condition is determined based on acquiredenvironmental parameter information, the purpose of performing safetycontrol on the intelligent connected vehicle based on a result of theenvironmental risk assessment is realized, and the safety of the vehiclein different risk environments is ensured.

Other features and advantages of embodiments of the present applicationwill be set forth in the description below, and in part will becomeapparent from the description, or may be learned by practicing theembodiments of the present application. Purposes and other advantages ofthe technical solutions of the present application may be realized andacquired by structures specified in the specification, claims, anddrawings.

BRIEF DESCRIPTION OF DRAWINGS

Accompanying drawings are used to provide a further understanding oftechnical solutions of the embodiments of the present application, forma part of the specification, and are used to explain the technicalsolutions of the embodiments of the present application together withthe embodiments of the present application and are not intended to formlimitations on the technical solutions of the embodiments of the presentapplication.

FIG. 1 is a flowchart of a safety control method based on environmentalrisk assessment for the intelligent connected vehicle according to anembodiment of the present application.

FIG. 2 is a schematic diagram of a safety control system based onenvironmental risk assessment for the intelligent connected vehicleaccording to an embodiment of the present application.

FIG. 3 is a flowchart of a method for driving control using the systemshown in FIG. 2.

FIG. 4 is a structural diagram of a safety control system based onenvironmental risk assessment for the intelligent connected vehicleaccording to an embodiment of the present application.

DETAILED DESCRIPTION

In order to make purposes, technical solutions and advantages of theembodiments of the present application clearer, the embodiments of thepresent application will be described in detail below with reference tothe accompanying drawings. It should be noted that the embodiments ofthe present application and the features in the embodiments can becombined with each other randomly if there is no conflict.

FIG. 1 is a flowchart of a safety control method based on environmentalrisk assessment for an intelligent connected vehicle according to anembodiment of the present application. As shown in FIG. 1, the methodshown in FIG. 1 includes steps 101 to 103.

In step 101, when a vehicle is in an automatic driving mode, theenvironmental parameter information of the vehicle in a current drivingenvironment is acquired.

In step 102, a target driving control parameter that meets a preset safedriving condition under the current environmental parameter isdetermined.

In step 103, a current automatic driving level of the vehicle is managedby using the target driving control parameter.

According to the method provided in the embodiment of the presentapplication, a driving control parameter in the current environmentalcondition which meets the safe driving condition is determined based onthe acquired environmental parameter information, a purpose ofperforming safety control on the intelligent connected vehicle based ona result of environmental risk assessment is realized, and safety of thevehicle in different risk environments is ensured.

The method according to the embodiment of the present application isdescribed below.

In an exemplary embodiment, the target driving control parameters areobtained in the following mode, which includes:

prebuilding a corresponding relationship between each environmentalparameter and a driving control parameter, wherein a determination modeof the driving control parameter in different value intervals of thesame environmental parameter is recorded in each group of thecorresponding relationships; and

according to the corresponding relationships between the environmentalparameters and the driving control parameters, determining a targetdriving control parameter corresponding to the environmental parameterinformation in the current driving environment.

Herein, forms of the pre-built corresponding relationships include, butare not limited to, functions, charts, and the like.

The target driving control parameter can be determined more quickly andeffectively by using different value intervals in the correspondingrelationships, and calculation costs are simplified.

In an exemplary embodiment, the environmental parameter informationincludes at least one of the following:

an external ambient temperature T of the vehicle, a weather condition Win a driving area, an adhesion coefficient F of a road surface ahead fordriving, and a roughness R of a current road surface for driving:

the driving control parameter includes at least one of the following:

a maximum vehicle speed v_(max), a maximum engine power P_(max), amaximum steering wheel angle φ_(max), a maximum engine torque M_(max)and current minimum following distances L_(min) in different trafficscenarios.

By acquiring an external ambient temperature, a current vehicle speed, acurrent weather condition, an adhesion coefficient, and a roughness ofthe current road surface, through a comprehensive optimization of allthe above information, constraint information of the maximum vehiclespeed, the maximum steering wheel angle, the maximum engine torque, themaximum engine power and the minimum following distance of the currentvehicle is obtained, and finally, the constraint information is sent toa decision-maker to improve the safety of the vehicle in different riskenvironments.

In an exemplary embodiment, the environmental parameter information isobtained in the following mode, which includes:

using a temperature sensor installed outside the vehicle to acquire theambient temperature T;

acquiring geographic position information L of the vehicle andrequesting to acquire the weather condition W corresponding to thegeographic position information L;

determining an adhesion coefficient F of a road surface ahead fordriving by using image information I of the road surface in front of thevehicle collected by a camera installed inside the vehicle; and

determining a roughness R of the current road surface by using anunsprung mass acceleration sensor installed on a kingpin of each wheelof the vehicle.

FIG. 2 is a schematic diagram of a safety control system based onenvironmental risk assessment for the intelligent connected vehicleaccording to an embodiment of the present application. As shown in FIG.2, the shown system includes an information acquisition unit, a safetyunit, and a decision-maker. The information acquisition unit includes atemperature sensor, a GPS module, an Internet module, a camera, andunsprung mass acceleration sensors. The safety unit includes threefunctional modules: a road surface adhesion coefficient estimationfunctional module, a road surface roughness estimation functionalmodule, and a downgrading action regulator functional module.

The temperature sensor is installed outside the vehicle, collects theexternal ambient temperature T of the vehicle, and transmits thetemperature to the safety unit.

The GPS module acquires geographic position information L of the vehicleand transmits the geographic position information to the Internetmodule, and simultaneously acquires a vehicle speed v and transmits thevehicle speed to the safety unit.

The Internet module acquires weather condition information W (rain,snow, visibility) of the current region from the cloud according to thegeographic position information L of the vehicle and transmits theweather condition information to the safety unit.

The camera is installed at an upper position on the front windshieldinside the vehicle, obtains image information I of the road surface infront of the vehicle, and transmits the image information to the safetyunit.

The unsprung mass acceleration sensors are installed on a kingpin ofeach wheel of the vehicle and collect unsprung mass accelerationinformation a₁, a₂, a₃ a₄ of the vehicle and transmit the unsprung massacceleration information to the safety unit.

The safety unit first estimates, according to the image information I ofthe road surface in front of the vehicle collected by the camera, anadhesion coefficient F of the road surface in front of the vehicle byusing the road surface adhesion coefficient estimation functional moduleintegrated with an existing road surface adhesion coefficient machinelearning algorithm, estimate, according to the unsprung massacceleration information a₁, a₂, a₃, a₄ collected by the unsprung massacceleration sensors, a roughness R of the current road surface at thesame time by using the road surface roughness estimation functionalmodule integrated with an existing road surface roughness estimationmethod, and transmit the adhesion coefficient F of the road surface infront of the vehicle and the roughness R of the current road surface tothe downgrading action regulator functional module. According to theambient temperature T, the weather condition W, the adhesion coefficientF of the road surface, the roughness R of the road surface, thedowngrading action regulator functional module calculates, based on arule-based method, five constraint conditions for vehicle safety drivingincluding a maximum vehicle speed v_(Imax), a maximum steering wheelangle φ_(Imax), a maximum engine torque M_(Imax), a maximum engine powerP_(Imax) and a minimum following distance L_(Imax), and the aboveconstraint conditions are sent to the decision-maker.

After receiving the constraint conditions from the downgrading actionregulator, the decision-maker makes a decision based on the constraintconditions according to an existing decision algorithm.

Driving of the vehicle is directly constrained by contents ofenvironmental perception, compared with other methods for improvingvehicle safety, this method has fewer intermediate links and quickresponse and promotes driving safety by constraining rather thandirectly interfering with the vehicle's behaviors, which can not onlyimprove the driving safety of vehicles in various unfavorableenvironments, but also exert the initiative of lower-leveldecision-makers, and the system is more flexible and balanced.

In an exemplary embodiment, a maximum vehicle speed v_(T_max) and amaximum engine power P_(T_max) under a constraint of the ambienttemperature T are determined;

a maximum vehicle speed v_(W_max) and a minimum following distanceL_(W_min) under a constraint of the weather condition W are determined;

a maximum steering wheel angle φ_(F_max), a maximum engine torqueM_(F_max) and a minimum following distance L_(F_min) under a constraintof the adhesion coefficient F of the road surface ahead for driving aredetermined; and

a maximum vehicle speed v_(R_max) under a constraint of the roughness Rof the road surface on which the vehicle travels is determined.

It can be seen from the above that at least two driving controlparameters can be determined by the same environmental parameter, thatis, the same environmental parameter can affect the values of at leasttwo driving control parameters. Therefore, the determination by theabove method can make determined target driving control parameters moreaccurate.

In an exemplary embodiment, target driving control parameters aredetermined in a following mode, which includes:

v _(max)=min{v _(T_max) ,v _(W_max) ,v _(F_max) ,v _(R_max)};

φ_(max)=φ_(F_max);

M _(max) =M _(F_max),

P _(max) =P _(T_max); and

L _(min)=max{L _(W_min) ,L _(F_min)}.

After the target driving control parameters are obtained based ondifferent environmental parameters, the obtained determination resultsare synthesized to obtain the target driving control parameters forcontrolling automatic driving, which are used as reference data foradjusting an automatic driving level.

In an exemplary embodiment, the weather condition W includes at leasttwo dimensions which are selected from rain, snow, and visibility;

when the maximum vehicle speed v_(W_max) and the minimum vehiclefollowing distance L_(W_min) are determined according to the weathercondition W, the maximum vehicle speed v_(W_max) and the minimum vehiclefollowing distance L_(W_min) corresponding to each dimension in theweather condition are determined.

By acquiring values of driving control parameters in differentdimensions, the values of the driving control parameters can bedetermined more accurately and the accuracy of data can be improved.

The method according to the embodiments of the present application isdescribed below with an application example:

as shown in FIG. 2, the system includes an information acquisition unit,a safety unit, and a decision-maker. The information acquisition unitincludes a temperature sensor, a GPS module, an internet module, acamera, and an acceleration sensor. The safety unit includes threefunctional modules: a road surface adhesion coefficient estimationfunctional module, a road surface roughness estimation functionalmodule, and a downgrading action regulator functional module.

Take a two-axle four-wheel vehicle with the L3 automatic drivingfunction as an example, wherein 1, 2, 3, and 4 respectively representthe front-left, front-right, rear-left, and rear-right corners of thevehicle.

The temperature sensor is installed on the top of the vehicle (as remotefrom heating sources as possible), which converts temperatureinformation of the external environment into a digital signal T andsends the digital signal T to the safety unit.

The GPS module is installed on the vehicle's chassis, acquires thereal-time geographic position information of the vehicle, converts thegeographic position information into a digital signal L, and sends thedigital signal L to the Internet module. At the same time, the GPSmodule also collects current vehicle speed information of the vehicle,converts the vehicle speed information into a digital signal v, andsends the digital signal v to the safety unit.

The Internet module is installed inside the vehicle, with the receivedGPS signal, acquires weather information of the position where thevehicle is located from the cloud, converts the weather information intoa digital signal W, and sends the digital signal W to the safety unit.

The camera is installed right above the front windshield vehicle,acquires image information I of the road surface in front of thevehicle, and sends the image information I to the safety unit.

The unsprung mass acceleration sensors are respectively installed on akingpin of each wheel of the vehicle and collect unsprung massacceleration information a₁, a₂, a₃, a₄ of the vehicle and send theunsprung mass acceleration information to the safety unit.

After the safety unit receives the information transmitted by eachmodule in the information acquisition unit, three functional modules,namely, the road surface adhesion coefficient estimation functionalmodule, the road surface roughness estimation functional module, and thedowngrading action regulator functional module are used for performinginformation processing to finally generate five vehicle safe drivingconstraint conditions, namely, a maximum vehicle speed v_(Imax), amaximum steering wheel angle φ_(Imax), a maximum engine torque M_(Imax),a maximum engine power P_(Imax) and a minimum following distanceL_(Imin), are generated, and sends the above constraint conditions tothe decision-maker.

The road surface adhesion coefficient estimation functional modulereceives the image information I of the road surface in front of thevehicle sent by the camera to the safety unit, uses a method in thearticle “Neural Network-based road friction using road weatherinformation” written by Minges Florian of Chalmers University ofTechnology in Sweden in 2020 to estimate the adhesion coefficient F ofthe road surface in front of the vehicle through the image information Iof the road surface in front of the vehicle, and sends the adhesioncoefficient F to the functional module of the downgrading actionregulator.

The road surface roughness estimation module receives the vehicleunsprung mass acceleration information a₁, a₂, a₃, a₄ sent by theunsprung mass acceleration sensors to the safety unit, uses the methodin the article “Online classification of road roughness conditions withvehicle unsprung mass acceleration by sliding time window” published byYu Wenhao of Jiangsu University in China in 2018 through the vehicleunsprung mass acceleration information a₁, a₂, a₃, a₄ to estimate theroughness R of the current road surface of the vehicle and sends theroughness to the downgrading action regulator functional module.

The downgrading action regulator receives the external ambienttemperature T, the current vehicle speed v, and the current weathercondition W sent to the safety unit by the temperature sensor, the GPSmodule, and the Internet module, and the adhesion coefficient F of theroad surface in front of the vehicle and the roughness R of the currentroad surface which are sent by the road surface adhesion coefficientestimation functional module. According to the safety requirements, fiveconstraint conditions for vehicle safety driving, namely the maximumvehicle speed v_(Imax), the maximum steering wheel angle φ_(Imax), themaximum engine torque M_(Imax), the maximum engine power P_(Imax) andthe minimum following distance L_(Imin), are calculated according toestablished mapping rules, and the above constraint conditions are sentto the decision-maker.

The constraint condition of the maximum vehicle speed v_(Imax) iscalculated by using the external ambient temperature T, the currentweather condition W, the adhesion coefficient F of the road surface infront of the vehicle, and the roughness R of the current road surface ofthe vehicle. The constraint condition of the maximum steering wheelangle φ_(Imax) is calculated by using the adhesion coefficient F of theroad surface in front of the vehicle. The constraint condition of themaximum engine torque M_(Imax) is calculated by using the adhesioncoefficient F of the road surface in front of the vehicle. Theconstraint condition of the maximum engine power P_(Imax) is calculatedby the external ambient temperature T. The constraint condition of theminimum following distance L_(Imin) is calculated by using the currentweather condition W.

FIG. 3 is a flowchart of a method for driving control using the systemshown in FIG. 2. As shown in FIG. 3, the method includes steps 1 to 9.

In step 1, a temperature sensor collects an external ambient temperatureT of a vehicle and sends the external ambient temperature to the safetyunit.

In step 2, a GPS module acquires a current position L of the vehicle andsends the current position to an Internet module. At the same time, theGPS module acquires a current vehicle speed v of the vehicle and sendsthe current vehicle speed to the safety unit.

In step 3, the Internet module acquires weather information W of thecurrent position according to the current position L of the vehicle andsends the weather information to the safety unit.

In step 4, a camera collects image information I of a road surface infront of the vehicle and sends the image information to the safety unit.

In step 5, in the safety unit, based on the image information I of theroad surface in front of the vehicle, a road surface adhesioncoefficient estimation functional module uses a method in the article“Neural network-based road friction using road weather information” byMinges Florian of Chalmers University of Technology in Sweden in 2020 toestimate the adhesion coefficient F of the road surface in front of thevehicle and sends the adhesion coefficient to a degraded actionregulator functional module.

In step 6, unsprung mass acceleration sensors collect unsprung massacceleration information a₁, a₂, a₃, a₄ of the vehicle and send theunsprung mass acceleration information to the safety unit.

In step 7, in the safety unit, based on the vehicle unsprung massacceleration information a₁, a₂, a₃, a₄, a road surface roughnessestimation functional module uses a method in the article “Onlineclassification of road roughness conditions with vehicle unsprung massacceleration by sliding time window” by Yu Wenhao of Jiangsu Universityin China in 2018 to estimate the roughness R of the current road surfaceof the vehicle and sends the roughness to a downgrading action regulatorfunctional module.

In step 8, in the safety unit, the downgrading action regulatorfunctional module receives the external ambient temperature T, thecurrent vehicle speed v, the current weather condition W sent to thesafety unit by the temperature sensor, the GPS module and the Internetmodule, the adhesion coefficient F of the road surface in front of thevehicle sent by the road surface adhesion coefficient estimationfunctional module and the roughness R of the current road surface sentby the road surface roughness estimation functional module. According tothe safety requirements, five constraint conditions for vehicle safetydriving, namely the maximum vehicle speed v_(Imax), the maximum steeringwheel angle φ_(Imax), the maximum engine torque M_(Imax), the maximumengine power P_(Imax) and the minimum following distance L_(Imin), arecalculated according to established mapping rules.

Forms of the mapping rules include but are not limited to, functions,charts, and the like. Taking the form of function as an example, thedowngrading action regulator gives constraint conditions for vehiclesafe driving according to the following flow. For any vehicle, theremust be five types of basic information, namely, a maximum vehicle speedv_(max), a maximum engine power P_(max), a maximum steering wheel angleψ_(max), a maximum engine torque M_(max) and current minimum followingdistances L_(min) in different traffic scenarios. Based on the fivetypes of basic information, firstly,

(1) the downgrading action regulator calculates a constraint conditionof a maximum vehicle speed v_(T_max) (m/s) limited by temperature and aconstraint condition of maximum engine power P_(T_max) (watt) limited bytemperature according to the external ambient temperature T sent by thetemperature sensor based on the following equations respectively:

$V_{T\_\max} = \left\{ \begin{matrix}{v_{\max} + {2\left( {T + 10} \right)}} & {T \leq {- 10}} \\v_{\max} & {{- 10} < T < 30} \\{v_{\max} - {2\left( {T - 30} \right)}} & {T > 30}\end{matrix} \right.$ $P_{T\_\max} = \left\{ \begin{matrix}{0.8P_{\max}} & {T \leq {- 10}} \\P_{\max} & {{- 10} < T < 30} \\{0.9P_{\max}} & {T > 30}\end{matrix} \right.$

wherein relationships between the constraint condition of the maximumvehicle speed v_(T_max) limited by temperature the maximum engine powerP_(T_max) limited by temperature and the ambient temperature T include,but are not limited to, the relationships described by theabove-mentioned equations.

(2) The downgrading action regulator calculates, according to thecurrent weather condition W sent by the Internet module, a constraintcondition of a maximum vehicle speed v_(W_i_max) (m/s) limited byweather and a constraint condition of a minimum following distanceL_(W_i_min) (m) limited by weather. The weather W includes three types:rain, snow, and visibility, which are expressed as

W=[r,s,f]^(T)

wherein r represents rainfall (mm/12 hours), s represents snowfall(mm/12 hours), f represents visibility (m), and in v_(W_i_max) andL_(W_i_min), iεW. Then the constraint condition of the maximum speedv_(W_i_max) limited by weather corresponding to different types ofweather environments is:

$V_{{W\_ r}{\_\max}} = \left\{ \begin{matrix}v_{\max} & {r \leq 5} \\{0.95v_{\max}} & {5 < r < 15} \\{0.9v_{\max}} & {15 < r < 50} \\{0.8v_{\max}} & {r > 50}\end{matrix} \right.$ $V_{{W\_ s}{\_ max}} = \left\{ \begin{matrix}{0.95v_{\max}} & {s \leq 1} \\{0.9v_{\max}} & {1 < s < 3} \\{0.85v_{\max}} & {3 < s < 6} \\{0.8v_{\max}} & {s > 6}\end{matrix} \right.$ $V_{{W\_ f}{\_ max}} = \left\{ \begin{matrix}20 & {f \leq 50} \\40 & {50 < f < 100} \\60 & {100 \leq f < 200} \\80 & {200 \leq f \leq 500}\end{matrix} \right.$

The constraint condition of the minimum following distance L_(W_i_mix)limited by weather corresponding to different types of weatherenvironments is:

$L_{{W\_ r}{\_ min}} = \left\{ \begin{matrix}L_{\min} & {r \leq 5} \\{1.1L_{\min}} & {5 < r < 15} \\{1.3L_{\min}} & {15 < r < 50} \\{1.5L_{\min}} & {r > 50}\end{matrix} \right.$ $L_{{W\_ s}{\_ min}} = \left\{ \begin{matrix}L_{\min} & {s \leq 1} \\{1.2L_{\min}} & {1 < s < 3} \\{1.4L_{\min}} & {3 < s < 6} \\{1.6L_{\min}} & {s > 6}\end{matrix} \right.$ $L_{{W\_ f}{\_ min}} = \left\{ \begin{matrix}50 & {f \leq 100} \\100 & {100 < f < 200} \\150 & {f \geq 200}\end{matrix} \right.$

wherein relationships between the constraint condition of the maximumvehicle speed v_(W_i_max) limited by weather, the constraint conditionof the minimum following distance L_(W_i_min) limited by weather and thecurrent weather condition W include, but are not limited to, therelationships described by the above-mentioned equations.

(3) According to the following equations, the downgrading actionregulator calculates, according to the adhesion coefficient F of the radsurface in front of the vehicle sent by the road surface adhesioncoefficient estimation functional module, a constraint condition of amaximum vehicle speed v_(F_max) limited by the adhesion coefficient ofthe road surface in front of the vehicle, a constraint condition of amaximum steering wheel angle φ_(F_max) limited by the adhesioncoefficient of the road surface in front of the vehicle, a constraintcondition of a maximum engine torque M_(F_max) limited by the adhesioncoefficient of the road surface in front of the vehicle and a constraintcondition of a minimum following distance L_(F_min) limited by theadhesion coefficient of the road surface in front of the vehicle:

$V_{F\_ max} = {\frac{1}{2}\left( {1 + {2F}} \right)V_{\max}}$$\varphi_{F\_\max} = {10{\sin^{- 1}\left( \frac{Fgl}{V} \right)}}$$M_{F\_\max} = {\frac{1}{3}\left( {1 + {2F}} \right)M_{\max}}$$L_{{W\_ F}{\_ min}} = \left\{ \begin{matrix}{1.8L_{\min}} & {F \leq 0.2} \\{1.6L_{\min}} & {0.2 < F < 0.4} \\{1.4L_{\min}} & {0.4 \leq F < 0.6} \\{1.2L_{\min}} & {0.6 \leq F < 0.8} \\L_{\min} & {0.8 \leq F \leq 1.}\end{matrix} \right.$

wherein v is a current vehicle speed sent by the GPS module, g is thegravity acceleration value, and I is the wheelbase of the vehicle. Therelationships between the constraint condition of the maximum vehiclespeed v_(F_max) limited by the adhesion coefficient of the road surfacein front of the vehicle, the constraint condition of the maximumsteering wheel angle φ_(F_max) limited by the adhesion coefficient ofthe road surface in front of the vehicle, the constraint condition ofthe maximum engine torque M_(F_max) limited by the adhesion coefficientof the road surface in front of the vehicle and the constraint conditionof the minimum following distance L_(F_min) limited by the adhesioncoefficient of the road surface in front of the vehicle, and theadhesion coefficient F of the road surface in front of the vehicleinclude, but not limited to, the relationships described by theabove-mentioned equations.

(4) According to the roughness R of the current road surface of thevehicle sent by the road surface roughness estimation functional module,the downgrading action regulator calculates a constraint condition of amaximum speed v_(R_max) limited by the roughness of the current roadsurface of the vehicle according to the following equation:

v _(R_max) =v _(max)−2 log₂(R)

wherein a relationship between the constraint condition of the maximumvehicle speed v_(R_max) limited by the roughness of the current roadsurface of the vehicle and the roughness R of the current road surfaceof the vehicle includes, but is not limited to, the relationshipdescribed by the above-mentioned equation.

(5) The downgrading action regulator calculates, according to thefollowing equations, five final constraint conditions for vehicle safetydriving, namely, the maximum vehicle speed v_(Imax), the maximumsteering wheel angle φ_(Imax), the maximum engine torque M_(Imax), themaximum engine power P_(Imax) and the minimum following distanceL_(Imin), and sends them to the decision maker:

v _(Imax)=min{v _(T_max) ,v _(W_r_max) ,v _(W_s_max) ,v _(W_f_max) ,V_(F_max) ,V _(R_max)}

φ_(Imax)=φ_(F_max)

M _(Imax) =M _(F_max)

P _(Imax) =P _(T_max)

L _(Imin)=max{L _(W_r_min) ,L _(W_s_min) ,L _(W_f_min) ,L _(F_min)}

In step 9, the decision maker makes corresponding downgrading actionsaccording to an existing planning method through the constraintconditions output by the downgrading action regulator, so as to minimizethe risk of the vehicle in the current environment.

According to the method provided in the embodiments of the presentapplication, a safety control scheme based on environmental riskassessment for an intelligent connected vehicle is provided. In thisscheme, the external ambient temperature, the current vehicle speed, thecurrent weather condition, the road image ahead and the unsprung massacceleration of the vehicle are acquired respectively by the temperaturesensor, the GPS module, the Internet module, the camera and the unsprungmass acceleration sensors. The adhesion coefficient and the roughness ofthe current road surface are estimated by using existing road surfaceadhesion coefficient and road surface roughness estimation methods,finally the downgrading action regulator optimizes all the aboveinformation comprehensively to obtain the constraint information of themaximum vehicle speed, the constraint information of the maximumsteering wheel angle, the constraint information of the maximum enginetorque, the constraint information of the maximum engine power and theconstraint information of the minimum following distance, and finallysends the constraint information to the decision maker to improve thesafety of the vehicle in different risk environments.

FIG. 4 is a structural diagram of a safety control system based onenvironmental risk assessment for intelligent connected vehicleaccording to an embodiment of the present application. Referring to FIG.4, the system includes:

an acquisition module configured to acquire environmental parameterinformation of a vehicle in a current driving environment when a vehicleis in an automatic driving mode;

a determination module configured to determine a target driving controlparameter which meets a preset safe driving condition under the currentenvironmental parameter; and

a management module configured to manage a current automatic drivinglevel of the vehicle by using the target driving control parameter.

According to the method provided in the embodiment of the presentapplication, the driving control parameter in the current environmentalcondition which meets the safe driving condition is determined based onacquired environmental parameter information, the purpose of performingsafety control on the intelligent connected vehicle based on a result ofthe environmental risk assessment is realized, and safety of the vehiclein different risk environments is ensured.

In an embodiment of the present application, a storage medium with acomputer program stored therein is provided, wherein when being run, thecomputer program is configured to perform to any one of above-mentionedmethods.

In an embodiment of the present application, an electronic deviceincluding a memory and a processor is provided, a computer program isstored in the memory, and the processor is configured to run thecomputer program to perform any one of the above-mentioned methods.

It can be understood by those of ordinary skills in the art that all orsome steps in the method disclosed above and functional modules/units inthe system and the apparatus may be implemented as software, firmware,hardware, and proper combinations thereof. In a hardware implementationmode, division of the functional modules/units mentioned in the abovedescription is not necessarily a division corresponding to physicalcomponents. For example, a physical component may have multiplefunctions, or multiple physical components may cooperate to execute afunction or a step. Some components or all components may be implementedas software executed by a processor such as a digital signal processoror a microprocessor, or implemented as hardware, or implemented as anintegrated circuit such as an application specific integrated circuit.Such software may be distributed in a computer-readable medium, and thecomputer-readable medium may include a computer storage medium (or anon-transitory medium) and a communication medium (or a transitorymedium). As known to those of ordinary skills in the art, the termcomputer storage medium includes volatile and nonvolatile and removableand irremovable media implemented in any method or technology forstoring information (for example, a computer-readable instruction, adata structure, a program module, or other data). The computer storagemedium includes, but not limited to, a random access memory (RAM), aread only memory (ROM), an Electrically Erasable Programmable Read-OnlyMemory (EEPROM), a flash memory or other memory technologies, a CD-ROM,a Digital Video Disk (DVD) or other compact discs, a cassette, amagnetic tape, a disk memory or other magnetic storage devices, or anyother medium configurable to store expected information and accessiblefor a computer. In addition, it is known to those of ordinary skills inthe art that the communication medium usually includes acomputer-readable instruction, a data structure, a program module, orother data in a modulated data signal of, such as, a carrier or anothertransmission mechanism, and may include any information transmissionmedium.

1. A safety control method based on environmental risk assessment for anintelligent connected vehicle, comprising: acquiring environmentalparameter information of a vehicle in a current driving environment whenthe vehicle is in an automatic driving mode; determining a targetdriving control parameter which meets a preset safe driving conditionunder the current environmental parameter; and managing a currentautomatic driving level of the vehicle by using the target drivingcontrol parameter.
 2. The method according to claim 1, wherein thetarget driving control parameter is obtained by: pre-building acorresponding relationship between each environmental parameter and adriving control parameter, wherein a determination mode of a drivingcontrol parameter for a same environmental parameter in different valueintervals is recorded in each group of the corresponding relationships;and according to the corresponding relationships between theenvironmental parameters and the driving control parameters, determiningthe target driving control parameter corresponding to the environmentalparameter information in the current driving environment.
 3. The methodaccording to claim 1, wherein: the environmental parameter informationcomprises at least one of the following: an external ambient temperatureT of the vehicle, a weather condition W in a driving area, an adhesioncoefficient F of a road surface ahead for driving and a roughness R of acurrent road surface for driving; the driving control parametercomprises at least one of the following: a maximum vehicle speedv_(max), a maximum engine power P_(max), a maximum steering wheel angleφ_(max), a maximum engine torque M_(max) and a current minimum followingdistance L_(min) in different traffic scenarios.
 4. The method accordingto claim 3, wherein the environmental parameter information is obtainedby: using a temperature sensor installed outside the vehicle to acquirethe external ambient temperature T; acquiring geographic positioninformation L of the vehicle and requesting to acquire the weathercondition W corresponding to the geographic position information L;determining the adhesion coefficient F of the road surface in front ofthe vehicle by using image information I of the road surface in front ofthe vehicle collected by a camera installed inside the vehicle; anddetermining the roughness R of the current road surface by using anunsprung mass acceleration sensor installed on a kingpin of each wheelof the vehicle.
 5. The method according to claim 3, comprising:determining a maximum vehicle speed v_(T_max) and a maximum engine powerP_(T_max) under a constraint of the external ambient temperature T;determining a maximum vehicle speed v_(W_max) and a minimum followingdistance L_(T_min) under a constraint of the weather condition W;determining a maximum steering wheel angle φ_(F_max) a maximum enginetorque M_(F_max) and a minimum following distance L_(F_min) under aconstraint of the adhesion coefficient F of the road surface ahead fordriving; and determining a maximum vehicle speed v_(R_max) under aconstraint of the roughness R of the road surface on which the vehicletravels.
 6. The method according to claim 5, wherein the target drivingcontrol parameter is determined in the following manner:v _(max)=min{v _(T_max) ,v _(W_max) ,v _(F_max) ,v _(R_max)};φ_(max)=φ_(F_max);M _(max) =M _(F_max),P _(max) =P _(T_max); andL _(min)=max{L _(W_min) ,L _(F_min)}.
 7. The method according to claim5, wherein: the weather condition W comprises at least two dimensionswhich are selected from rain, snow and visibility: when the maximumvehicle speed v_(W_max) and the minimum vehicle following distanceL_(W_min) are determined according to the weather condition W, themaximum vehicle speed v_(W_max) and the minimum vehicle followingdistance L_(W_min) corresponding to each dimension in the weathercondition are determined.
 8. A safety control system based onenvironmental risk assessment for an intelligent connected vehicle,comprising: an acquisition module configured to acquire environmentalparameter information of a vehicle in a current driving environment whenthe vehicle is in an automatic driving mode; a determination moduleconfigured to determine a target driving control parameter which meets apreset safe driving condition under the current environmental parameter;and a management module configured to manage a current automatic drivinglevel of the vehicle by using the target driving control parameter.
 9. Astorage medium in which a computer program is stored, wherein when beingrun, the computer program is configured to perform the method accordingto claim
 1. 10. An electronic device comprising a memory and aprocessor, wherein a computer program is stored in the memory, and theprocessor is configured to run the computer program to perform themethod according to claim
 1. 11. The method according to claim 2,wherein: the environmental parameter information comprises at least oneof the following: an external ambient temperature T of the vehicle, aweather condition W in a driving area, an adhesion coefficient F of aroad surface ahead for driving and a roughness R of a current roadsurface for driving; the driving control parameter comprises at leastone of the following: a maximum vehicle speed v_(max), a maximum enginepower P_(max), a maximum steering wheel angle φ_(max), a maximum enginetorque M_(max) and a current minimum following distance L_(min) indifferent traffic scenarios.